Bringing new drugs to market is a lengthy and expensive endeavor. To increase the number of affordable new drugs available to consumers at reasonable costs, the efficiency of the drug development process must be greatly improved. We aim to develop a novel tool that addresses this challenge by rapid identification of drug targets and drug candidates for any given clinical phenotype utilizing our newly developed computational method. This method is based on a hypothesis that the tissue pattern of the expression of drug targets is essential for the unbiased matching of phenotypes to potential drugs. Matching complex molecular biomarkers of a disease to drugs thus depends on knowledge of the complete molecular signature (polypharmacologic fingerprint) of the drug or drug combination. Our approach is based on integration of large datasets from chemical and 'omics' databases and developing algorithms to link clinical phenotypes with drugs and drug-like chemicals. This is achieved by identifying the polypharmacologic profile of bioactive drugs and matching this drug-based profile to a phenotype-based tissue profile of a disease, which is derived from the computational analysis of 'omics' data. We have already developed and validated the 'drug' module of this system. Here we propose to (a) develop the 'phenotype' module, which will include the phenotype-tissue-target data via integration of clinical phenotypes, tissue-specific target expression, and GWAS and microarray data; (b) develop 'matching' algorithms for the modules that will build a direct link between drugs and clinical phenotypes as well as a ranking system for those matches; (c) validate this system using well-established clinical phenotypes and drugs for their treatment. When completed, the proposed research will result in a system that will allow users to instantly identify the ranked list of predicted drugs for any given clinical phenotype. Alternatively, it can be used to identify a list f phenotypes associated with any given drug/compound or drug/compound combination. This addresses a major challenge in the drug development process, improving the productivity and efficiency of the drug discovery phase, and it provides a useful and highly desired tool for the pharmaceutical industry and for academic research. Ultimately this technology will help in understanding the genomic contributions to the biology of disease and to accelerate the use of genomics to advance the science of medicine and the effectiveness of healthcare.